SpringerLink
Log in

Investigating factors affecting teachers' training through mobile learning: Task technology fit perspective

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

Mobile learning has ushered in a transformative era in education, compelling educational institutions to reimagine their pedagogical approaches. This shift is particularly evident in teacher training programs, where mobile learning is increasingly integrated into traditional education. The success of these integrated systems hinges on educators' willingness to adapt to these evolving paradigms. Nevertheless, a notable gap exists in the research landscape concerning the challenges of adopting mobile learning in teacher training and its consequential impact on teachers' professional capabilities. This study aims to bridge this gap by identifying the factors influencing teachers' satisfaction and performance within mobile learning training courses and establishing the intricate relationships between these variables. This study was conducted in a quantitative research framework and collected data from 563 schoolteachers through an online survey. These respondents were actively engaged in mobile-based training courses at the Provincial Institute for Teacher Education (PITE) Sindh during their mandatory training programs in the academic year 2022-23. Structural equation modeling was employed to analyze the proposed hypotheses rigorously. The study's findings unveil a robust and significant nexus between several critical factors and educators' experiences when utilizing mobile learning for training. Specifically, content quality, information quality, system quality, prior experiences, and mobile self-efficacy contributed strongly to task-technology fit, ultimately enhancing teachers' engagement, and yielding improved outcomes. Moreover, the study elucidates a clear correlation between factors encompassing understanding, Instructors' prompt feedback, teachers' expectations, and instructor quality within the context of training course design. These factors collectively positively influence teachers’ satisfaction and performance, enhancing content knowledge, pedagogical skills, and professional dispositions. This holistic approach to mobile learning positively influences teachers’ satisfaction and, ultimately, enhances teachers' overall performance. This study provides valuable insights to guide educators, institutions, and policymakers in effectively embracing and implementing mobile learning to benefit teachers and, ultimately, the broader field of education.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Canada)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

Not applicable.

Reference

  • Abu-Al-Aish, A., & Love, S. (2013). Factors influencing students’ acceptance of m-learning: An investigation in higher education. International Review of Research in Open and Distributed Learning, 14(5), 82–107.

    Article  Google Scholar 

  • Ahmad, I. (2014). Critical analysis of the problems of education in Pakistan: Possible solutions. International Journal of Evaluation and Research in Education, 3(2), 79–84.

  • Ahmed, M. S. (2016). Technology acceptance of smartphones as mobile learning tools: A contextual comparative study of engineering and education colleges. University of Canterbury. https://hdl.handle.net/10092/13096. https://doi.org/10.26021/9486

  • Al-Maatouk, Q., Othman, M. S., Aldraiweesh, A., Alturki, U., Al-Rahmi, W. M., & Aljeraiwi, A. A. (2020). Task-technology fit and technology acceptance model application to structure and evaluate the adoption of social media in academia. IEEE Access: Practical Innovations, Open Solutions, 8, 78427–78440.

    Article  Google Scholar 

  • Al-Rahmi, W. M., & Zeki, A. M. (2017). A model of using social media for collaborative learning to enhance learners’ performance on learning. Journal of King Saud University-Computer and Information Sciences, 29(4), 526–535.

    Article  Google Scholar 

  • Al-Rahmi, W. M., Othman, M. S., & Musa, M. A. (2014). The improvement of students’ academic performance by using social media through collaborative learning in Malaysian higher education. Asian Social Science, 10(8), 210.

    Google Scholar 

  • Al-Rahmi, W., Mugahed, Othman, M. S., & Yusuf, L. M. (2015). Effect of engagement and collaborative learning on satisfaction through the use of social media on Malaysian higher education. Research Journal of Applied Sciences Engineering and Technology, 9(12), 1132–1142.

    Article  Google Scholar 

  • Al-Rahmi, A. M., Shamsuddin, A., & Alismaiel, O. A. (2020). Task-technology fit model: The factors affecting students’ academic performance in higher education. Universal Journal of Educational Research, 8, 6831–6843.

    Article  Google Scholar 

  • Al-Rahmi, A. M., Al-Rahmi, W. M., Alturki, U., Aldraiweesh, A., Almutairy, S., & Al-Adwan, A. S. (2021a). Exploring the factors affecting mobile learning for sustainability in higher education. Sustainability, 13(14), 7893.

    Article  Google Scholar 

  • Al-Rahmi, A. M., Shamsuddin, A., Alturki, U., Aldraiweesh, A., Yusof, F. M., Al-Rahmi, W. M., & Aljeraiwi, A. A. (2021b). The influence of information system success and technology acceptance model on social media factors in education. Sustainability, 13(14), 7770.

    Article  Google Scholar 

  • Al-Rahmi, A. M., Al-Rahmi, W. M., Alturki, U., Aldraiweesh, A., Almutairy, S., & Al-Adwan, A. S. (2022). Acceptance of mobile technologies and M-learning by university students: An empirical investigation in higher education. Education and Information Technologies, 27(6), 7805–7826.

  • Al-Rahmi, A. M., Shamsuddin, A., Wahab, E., Al-Rahmi, W. M., Alturki, U., Aldraiweesh, A., & Almutairy, S. (2022b). Integrating the role of UTAUT and TTF model to evaluate social media use for teaching and learning in higher education. Frontiers in Public Health, 10, 905968.

    Article  Google Scholar 

  • Al-Rahmi, W., Mugahed, Al-Adwan, A. S., Al-Maatouk, Q., Othman, M. S., Alsaud, A. R., Almogren, A. S., & Al-Rahmi, A. M. (2023). Integrating communication and task–technology fit theories: The adoption of digital media in learning. Sustainability, 15(10), 8144.

    Article  Google Scholar 

  • Alalwan, N., Al-Rahmi, W. M., Alfarraj, O., Alzahrani, A., Yahaya, N., & Al-Rahmi, A. M. (2019). Integrated three theories to develop a model of factors affecting students’ academic performance in higher education. IEEE Access, 7, 98725–98742.

    Article  Google Scholar 

  • Alamri, M. M., Almaiah, M. A., & Al-Rahmi, W. M. (2020). The role of compatibility and task-technology fit (TTF): On social networking applications (SNAs) usage as sustainability in higher education. IEEE Access: Practical Innovations, Open Solutions, 8, 161668–161681.

    Article  Google Scholar 

  • Alawani, A. S., & Singh, A. D. (2017). A smart mobile learning conceptual framework for professional development of UAE in-service teachers. International Journal of Management and Applied Research, 4(3), 146–165.

    Article  Google Scholar 

  • Alghamdi, N. (2021). TIMSS 2019 report - (First insights into mathematics and science achievements of Saudi Arabia students in grades 4 and 8 in an international context. Netherlands: International Association for the Evaluation of Educational Achievement. Retrieved from https://policycommons.net/artifacts/1665877/timss-2019-report/2397526/. Accessed 21 Dec 2023.

  • Alharbi, S. & Drew, S. (2014). Using the technology acceptance model in understanding academics' behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications(IJACSA), 5(1). https://doi.org/10.14569/IJACSA.2014.050120

  • Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. (2019). Applying the UTAUT model to explain the students’ acceptance of mobile learning system in higher education. IEEE Access: Practical Innovations, Open Solutions, 7, 174673–174686.

    Article  Google Scholar 

  • Almodaires, A. A., Almutairi, F. M., & Almsaud, T. E. A. (2021). Pre-service teachers’ perceptions of the effectiveness of microsoft teams for remote learning. International Education Studies, 14(9), 108–121.

    Article  Google Scholar 

  • Almogren, A. S. (2023). Art students’ interaction and engagement: The mediating roles of collaborative learning and actual use of social media affect academic performance. Education and Information Technologies, 28, 14423–14451. https://doi.org/10.1007/s10639-023-11735-3

  • Almogren, A. S., & Aljammaz, N. A. (2022). The integrated social cognitive theory with the TAM model: The impact of M-learning in King Saud University art education. Frontiers in Psychology, 13, 1050532.

    Article  Google Scholar 

  • Alqahtani, M. A., Alamri, M. M., Sayaf, A. M., & Al-Rahmi, W. M. (2022). Exploring student satisfaction and acceptance of e-learning technologies in Saudi higher education. Frontiers In Psychology, 13, 939336.

    Article  Google Scholar 

  • Alturki, U., & Aldraiweesh, A. (2023). Integrated TTF and self-determination theories in higher education: The role of actual use of the massive open online courses. Frontiers in Psychology, 14, 1108325.

    Article  Google Scholar 

  • Alyoussef, I. Y. (2021). E-Learning acceptance: The role of task–technology fit as sustainability in higher education. Sustainability, 13(11), 6450.

    Article  Google Scholar 

  • Alyoussef, I. Y. (2023). Acceptance of e-learning in higher education: The role of task-technology fit with the information systems success model. Heliyon, 9(3):e13751. https://doi.org/10.1016/j.heliyon.2023.e13751

  • Alzahrani, A. I., Mahmud, I., Ramayah, T., Alfarraj, O., & Alalwan, N. (2019). Modelling digital library success using the DeLone and McLean information system success model. Journal of Librarianship and Information Science, 51(2), 291–306.

    Article  Google Scholar 

  • Arain, A. A., Hussain, Z., Rizvi, W. H., & Vighio, M. S. (2016). Evaluating usability of M-learning application in the context of Higher Education Institute. In P. Zaphiris, & A. Ioannou (Eds.), Learning and collaboration technologies. LCT 2016. Lecture notes in computer science (Vol. 9753). Springer, Cham. https://doi.org/10.1007/978-3-319-39483-1_24

  • Arain, A. A., Hussain, Z., Rizvi, W. H., & Vighio, M. S. (2018a). An analysis of the influence of a mobile learning application on the learning outcomes of higher education students. Universal Access in the Information Society, 17(2), 325–334.

    Article  Google Scholar 

  • Arain, A. A., Hussain, Z., Vighio, M. S., & Rizvi, W. H. (2018b). Factors influencing acceptance of Mobile learning by higher education students in Pakistan. Sindh University Research Journal-SURJ (Science Series), 50(01), 141–146.

    Article  Google Scholar 

  • Arain, A. A., Hussain, Z., Rizvi, W. H., & Vighio, M. S. (2019). Extending UTAUT2 toward acceptance of mobile learning in the context of higher education. Universal Access in the Information Society, 18(3), 659–673.

    Article  Google Scholar 

  • Asghar, M. Z., Barberà, E., & Younas, I. (2021). Mobile learning technology readiness and acceptance among pre-service teachers in Pakistan during the COVID-19 Pandemic. Knowledge Management & E-Learning, 13(1), 83–101.

    Google Scholar 

  • Bagozzi, R. P., Yi, Y., & Nassen, K. D. (1998). Representation of measurement error in marketing variables: Review of approaches and extension to three-facet designs. Journal of Econometrics, 89(1–2), 393–421.

    Article  Google Scholar 

  • Bangert, A. W. (2004). The seven principles of good practice: A framework for evaluating on-line teaching. The Internet and Higher Education, 7(3), 217–232.

    Article  Google Scholar 

  • Baragash, R. S., & Al-Samarraie, H. (2018). Blended learning: Investigating the influence of engagement in multiple learning delivery modes on students’ performance. Telematics and Informatics, 35(7), 2082–2098.

    Article  Google Scholar 

  • Barclay, M. J., Smith, C. W., & Watts, R. L. (1995). The determinants of corporate leverage and dividend policies. Journal of Applied Corporate Finance, 7(4), 4–19.

    Article  Google Scholar 

  • Barker, A., Krull, G., & Mallinson, B. (2009). A proposed theoretical model for m-learning adoption in develo** countries, in mLearn 2005-4th World conference on mLearning Cape Town. In Mobile and Wireless Systems Beyond 3G: Managing New Business Opportunities M. Pagani, Ed.: Idea Group.

  • Beg, S. A., Lucas, A. M., Halim, W., & Saif, U. (2019). Beyond the basics: Improving post-primary content delivery through classroom technology. National Bureau of Economic Research.

    Google Scholar 

  • Black, G. S., & Kassaye, W. W. (2014). Do students learning styles impact student outcomes in marketing classes? Academy of Educational Leadership Journal, 18(4), 149.

    Google Scholar 

  • Butt, S., Mahmood, A., Saleem, S., Rashid, T., & Ikram, A. (2021). Students’ performance in online learning environment: The role of task technology fit and actual usage of system during COVID-19. Frontiers in Psychology, 12, 759227.

    Article  Google Scholar 

  • Can, Y., & Bardakci, S. (2022). Teachers’ opinions on (urgent) distance education activities during the pandemic period. Advances in Mobile Learning Educational Research, 2(2), 351–374.

    Article  Google Scholar 

  • Candrawati, K., Nuvriasari, A., Yulianto, E., Adijaya, K. D., & Farizy, N. (2023). The role of information quality on task technology fit and student academic performance. Entrepreneurship, Economics, and Business International Conference (EEBIC), 1(1). https://seminar.uad.ac.id/index.php/EEBIC/article/view/12575. Accessed July 2013.

  • Chang, C., Hwang, G., & Gau, M. (2022). Promoting students’ learning achievement and self-efficacy: A mobile chatbot approach for nursing training. British Journal of Educational Technology, 53(1), 171–188.

    Article  Google Scholar 

  • Chen, H. R., & Tseng, H. F. (2012). Factors that influence acceptance of web-based e-learning systems for the in-service education of junior high school teachers in Taiwan. Evaluation and Program Planning, 35(3), 398–406.

    Article  Google Scholar 

  • Council, B. (2018). Continuing professional development (CPD) options for primary school teachers (PSTs) at school and local level in Punjab. https://www.britishcouncil.pk/sites/default/files/cpd_options_for_psts_at_school_and_local_level_in_punjab_-_british_council_2018.pdf. Accessed July 2023.

  • Dahri, N. A., Vighio, M. S., & Dahri, M. H. (2019). A survey on technology supported collaborative learning tools and techniques in teacher education. International Conference on Information Science and Communication Technology (ICISCT), 2019, 1–9.

    Google Scholar 

  • Dahri, N. A., Vighio, M. S., Bather, J. D., & Arain, A. A. (2021). Factors influencing the acceptance of mobile collaborative learning for the continuous professional development of teachers. Sustainability, 13(23), 13222.

    Article  Google Scholar 

  • Dahri, N. A., Vighio, M. S., Al-Rahmi, W. M., & Alismaiel, O. A. (2022a). Usability evaluation of mobile app for the sustainable professional development of teachers. International Journal of Interactive Mobile Technologies, 16(16), 4–30. https://doi.org/10.3991/ijim.v16i16.32015

  • Dahri, N. A., Vighio, M. S., Alismaiel, O. A., & Al-Rahmi, W. M. (2022b). Assessing the impact of mobile-based training on teachers’ achievement and usage attitude. International Journal of Interactive Mobile Technologies (iJIM), 16(09), 107–129. https://doi.org/10.3991/ijim.v16i09.30519

  • Dahri, N. A., Al-Rahmi, W. M., Almogren, A. S., Yahaya, N., Vighio, M. S., & Al-Maatuok, Q. (2023a). Mobile-based training and certification framework for teachers’ professional development. Sustainability, 15(7), 5839.

    Article  Google Scholar 

  • Dahri, N. A., Al-Rahmi, W. M., Almogren, A. S., Yahaya, N., Vighio, M. S., Al-maatuok, Q., Al-Rahmi, A. M., & Al-Adwan, A. S. (2023b). Acceptance of mobile learning technology by teachers: influencing mobile self-efficacy and 21st-century skills-based training. Sustainability, 15(11), 8514.

    Article  Google Scholar 

  • DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of Management Information Systems, 19(4), 9–30.

    Article  Google Scholar 

  • Ekanayake, S. Y., & Wishart, J. (2015). Integrating mobile phones into teaching and learning: A case study of teacher training through professional development workshops. British Journal of Educational Technology, 46(1), 173–189.

    Article  Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  • Fuaddah, N., & Sifa’ul (2021). STudents’perceptions on the use of microsoft teams as an alternative online learning platform at sman 2 semarang in the academic year 2020–2021 (A Descriptive Case Study of the Eleventh Graders at SMAN 2 Semarang in the Academic Year 2020–2021). Universitas Islam Sultan Agung Semarang.

  • Fuaddah, N. S., & Maharani, M. M. (2021). Microsoft teams in the perspective of the users. Journal of Advanced Multidisciplinary Research, 2(2), 62–69.

    Article  Google Scholar 

  • Gloria, A., & Oluwadara, A. (2016). Influence of mobile learning training on pre-service social studies teachers’ technology and mobile phone self-efficacies. Journal of Education and Practice, 7(2), 74–79.

    Google Scholar 

  • Goh, C., Leong, C., Kasmin, K., Hii, P., & Tan, O. (2017). Students’ experiences, learning outcomes and satisfaction in e-learning. Journal of E-Learning and Knowledge Society, 13(2) Italian e-Learning Association. Retrieved December 20, 2023 from https://www.learntechlib.org/p/188116/

  • González-Pérez, L. I., & Ramírez-Montoya, M. S. (2022). Components of Education 4.0 in 21st century skills frameworks: systematic review. Sustainability, 14(3), 1493.

    Article  Google Scholar 

  • Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19, 213–236.

    Article  Google Scholar 

  • Gopal, R., Singh, V., & Aggarwal, A. (2021). Impact of online classes on the satisfaction and performance of students during the pandemic period of COVID 19. Education and Information Technologies, 26(6), 6923–6947.

    Article  Google Scholar 

  • Gurban, M. A., & Almogren, A. S. (2022). Students’ actual use of E-learning in higher education during the COVID-19 pandemic. SAGE Open, 12(2), 21582440221091250.

    Article  Google Scholar 

  • Guskey, T. R. (2000). Evaluating professional development. Corwin press.

    Google Scholar 

  • Guskey, T. R., & Yoon, K. S. (2009). What works in professional development? Phi Delta Kappan, 90(7), 495–500.

    Article  Google Scholar 

  • Hair, J., Jr., Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.

    Book  Google Scholar 

  • Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458.

    Article  Google Scholar 

  • Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24.

    Article  Google Scholar 

  • Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012a). The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications. Long Range Planning, 45(5–6), 320–340.

    Article  Google Scholar 

  • Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012b). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433.

    Article  Google Scholar 

  • Hall, T., & Connolly, C. (2019). Mobile learning in teacher education. TechTrends, 63(6), 644–646.

    Article  Google Scholar 

  • Hassanzadeh, A., Kanaani, F., & Elahi, S. (2012). A model for measuring e-learning systems success in universities. Expert Systems with Applications, 39(12), 10959–10966.

    Article  Google Scholar 

  • Henriksen, D., Mishra, P., & Fisser, P. (2016). Infusing creativity and technology in 21st century education: A systemic view for change. Journal of Educational Technology & Society, 19(3), 27–37.

    Google Scholar 

  • Jaggars, S. S., & Xu, D. (2016). How do online course design features influence student performance? Computers & Education, 95, 270–284.

    Article  Google Scholar 

  • Jamil, B. (2004). From Teacher Education to Professional Education Devleopment in Pakistan: A Position Paper. Retrieved July 20, 2023, from ITA: http://itacec.org/document/Teacher

  • Kauffman, H. (2015). A review of predictive factors of student success in and satisfaction with online learning. Research in Learning Technology, 23. https://doi.org/10.3402/rlt.v23.26507

  • Keengwe, J., & Bhargava, M. (2014). Mobile learning and integration of mobile technologies in education. Education and Information Technologies, 19(4), 737–746.

    Article  Google Scholar 

  • Khoiri, A., Komariah, N., Utami, R. T., Paramarta, V., & Sunarsi, D. (2021). 4Cs analysis of 21st century skills-based school areas. Journal of Physics: Conference Series, 1764(1), 12142.

    Google Scholar 

  • Koole, M., McQuilkin, J. L., & Ally, M. (2010). Mobile learning in distance education: Utility or futility. Journal of Distance Education, 24(2), 59–82. https://auspace.athabascau.ca/bitstream/handle/2149/2673/UMLAUT_M_MobiGlam_project.pdf?sequence=1&isAllowed=y. Accessed July 2023.

  • Li, X., & Zhu, W. (2022). System quality, information quality, satisfaction and acceptance of online learning platform among college students in the context of online learning and blended learning. Frontiers in Psychology, 13, 1054691.

    Article  Google Scholar 

  • Lin, H. F. (2010). An investigation into the effects of IS quality and top management support on ERP system usage. Total Quality Management, 21(3), 335–349.

    Article  Google Scholar 

  • Lin, W. S., & Wang, C. H. (2012). Antecedences to continued intentions of adopting e-learning system in blended learning instruction: A contingency framework based on models of information system success and task-technology fit. Computers & Education, 58(1), 88–99.

    Article  Google Scholar 

  • Liu, D., & Luo, J. (2022). College learning from classrooms to the internet: Adoption of the YouTube as supplementary tool in COVID-19 pandemic environment. Education and Urban Society, 54(7), 848–870.

    Article  Google Scholar 

  • Liu, F., Wu, X., Ge, S., Fan, W., & Zou, Y. (2021). Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 13753–13762). https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Exploring_and_Distilling_Posterior_and_Prior_Knowledge_for_Radiology_Report_CVPR_2021_paper.html. Accessed July 2023.

  • Mater, W., Matar, N., Alismaiel, O. A., Al Moteri, M. A., Youssef, A., & Al-Rahmi, W. M. (2021). Factors influencing the intention behind mobile wallet adoption: Perceptions of university students. Entrepreneurship and Sustainability Issues, 9(1), 447.

    Article  Google Scholar 

  • Mokhtar, S. A., Katan, H., & Hidayat-ur-Rehman, I. (2018). Instructors’ behavioural intention to use learning management system: An Integrated TAM Perspective. TEM Journal, 7(3), 513–525. https://doi.org/10.18421/TEM73-07

  • Morris, M. G., & Venkatesh, V. (2000). Age differences in technology adoption decisions: Implications for a changing work force. Personnel Psychology, 53(2), 375–403.

    Article  Google Scholar 

  • Navarro, M. M., Prasetyo, Y. T., Young, M. N., Nadlifatin, R., & Redi, A. A. N. P. (2021). The perceived satisfaction in utilizing learning management system among engineering students during the COVID-19 pandemic: Integrating task technology fit and extended technology acceptance model. Sustainability, 13(19), 10669.

    Article  Google Scholar 

  • Ohanu, I. B., Shodipe, T. O., Ohanu, C. M. G., & Anene-Okeakwa, J. E. (2023). System quality, technology acceptance model and theory of planned behaviour models: Agents for adopting blended learning tools. E-Learning and Digital Media, 20(3), 255–281.

    Article  Google Scholar 

  • Omotayo, F. O., & Haliru, A. (2020). Perception of task-technology fit of digital library among undergraduates in selected universities in Nigeria. The Journal of Academic Librarianship, 46(1), 102097.

    Article  Google Scholar 

  • Ozdamli, F. (2012). Pedagogical framework of m-learning. Procedia-Social and Behavioral Sciences, 31, 927–931.

    Article  Google Scholar 

  • Ozdamli, F., & Cavus, N. (2011). Basic elements and characteristics of mobile learning. Procedia-Social and Behavioral Sciences, 28, 937–942.

    Article  Google Scholar 

  • Ozdamli, F., & Uzunboylu, H. (2015). M-learning adequacy and perceptions of students and teachers in secondary schools. British Journal of Educational Technology, 46(1), 159–172.

  • Pal, D., & Vanijja, V. (2020). Perceived usability evaluation of Microsoft Teams as an online learning platform during COVID-19 using system usability scale and technology acceptance model in India. Children and Youth Services Review, 119, 105535.

    Article  Google Scholar 

  • Raffaghelli, J. E., Rodríguez, M. E., Guerrero-Roldán, A. E., & Bañeres, D. (2022). Applying the UTAUT model to explain the students’ acceptance of an early warning system in Higher Education. Computers & Education, 182, 104468.

    Article  Google Scholar 

  • Raven, A., & Park, C. W. (2015). Information quality as a determinant of task-technology fit in using communication technology for simple task. Issues in Information Systems, 16(1), 189–199. https://doi.org/10.48009/1_iis_2015_189-199

  • Rawal, S., Aslam, M., & Jamil, B. (2013). Teacher characteristics, actions and perceptions: what matters for student achievement in Pakistan? In CSAE Working Paper Series 2013-19, Centre for the Study of African Economies. University of Oxford.

  • Roldán, J. L., & Sánchez-Franco, M. J. (2012). Variance-based structural equation modeling: Guidelines for using partial least squares in information systems research. Research methodologies, innovations and philosophies in software systems engineering and information systems (pp. 193–221). IGI global.

  • SAT. (2017). Government of Sindh Standardized Achievement Test (SAT) Phase - V. https://www.iba-suk.edu.pk/. Accessed August 2023.

  • Sayaf, A. M., Alamri, M. M., Alqahtani, M. A., & Al-Rahmi, W. M. (2021). Information and communications technology used in higher education: An empirical study on digital learning as sustainability. Sustainability, 13(13), 7074.

    Article  Google Scholar 

  • Sayaf, A. M., Alamri, M. M., Alqahtani, M. A., & Alrahmi, W. M. (2022). Factors influencing University Students’ Adoption of digital learning technology in teaching and learning. Sustainability, 14(1), 493.

    Article  Google Scholar 

  • Schwarz, C., & Zhu, Z. (2015). The impact of student expectations in using instructional tools on student engagement: A look through the expectation disconfirmation theory lens. Journal of Information Systems Education, 26(1), 47.

    Google Scholar 

  • SESP. (2019). School Education Sector Plan and Roadmap for Sindh. https://seld.sesp-rsu.com/. Accessed July 2023.

  • Shah, F. U. H. (2013). EFfect of continuous professional development teachers programme on the performance of primary school teachers. Qurtuba University of Science and Information Technology KPK.

  • Shahzad, A., Hassan, R., Aremu, A. Y., Hussain, A., & Lodhi, R. N. (2021). Effects of COVID-19 in E-learning on higher education institution students: the group comparison between male and female. Quality & Quantity, 55, 805–826.

    Article  Google Scholar 

  • Sharples, M., Taylor, J., & Vavoula, G. (2005). Towards a theory of mobile learning. Proceedings of MLearn, 1(1), 1–9.

    Google Scholar 

  • Sindh, G. (2017). Continuous Professional Development (CPD) Model. Report. https://www.sindheducation.gov.pk/Contents/Menu/CPDModel.pdf. Accessed July 2023.

  • Singh, A. K., Rind, I. A., & Sabur, Z. (2020). Continuous professional development of school teachers. In P. M. Sarangapani & R. Pappu (Eds.), Handbook of education systems in South Asia. Global education systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-3309-5_31-1

  • So, H. J. (2012). Turning on mobile learning in Asia: Illustrative initiatives and policy implications. Policy Focus: UNESCO Working Paper Series on Mobile Learning, 1, 1–32.

    Google Scholar 

  • So, H.-J., Peng, D., Hair, J. F. J. F., Sarstedt, M., Ringle, C. M., Mena, J. A., Al-Rahmi, A. M., Al-Rahmi, W. M., Alturki, U., Aldraiweesh, A., Almutairy, S., Al-Adwan, A. S., Arain, A. A., Hussain, Z., Rizvi, W. H., Vighio, M. S., Krejcie, R. V, Morgan, D. W., Bentler, P. M., … SM, L. M. G. (2012). Applying the UTAUT model to explain the students’ acceptance of an early warning system in Higher Education. Sustainability, 13(4), 486–490.

  • Sun, K. S., Huang, C. N., & Chen, Y. P. (2009). A model of the determinants of purchase of home security and remote control equipments. IEEE International Conference on Industrial Engineering and Engineering Management, 2009, 1042–1046.

    Google Scholar 

  • Suzianti, A., & Paramadini, S. A. (2021). Continuance intention of e-learning: The condition and its connection with open innovation. Journal of Open Innovation: Technology Market and Complexity, 7(1), 97.

    Article  Google Scholar 

  • Tawafak, R. M., Al-Rahmi, W. M., Almogren, A. S., Al Adwan, M. N., Safori, A., Attar, R. W., & Habes, M. (2023a). Analysis of E-Learning System Use Using Combined TAM and ECT Factors. Sustainability, 15(14), 11100.

    Article  Google Scholar 

  • Tawafak, R. M., Alyoussef, I. Y., & Al-Rahmi, W. M. (2023b). Essential factors to improve student performance using an e-learning model: Review study. International Journal of Interactive Mobile Technologies (iJIM), 17(03), 160–176. https://doi.org/10.3991/ijim.v17i03.35727

  • Teo, T., Moses, P., Cheah, P. K., Huang, F., & Tey, T. C. Y. (2023). Influence of achievement goal on technology use among undergraduates in Malaysia. Interactive Learning Environments. https://doi.org/10.1080/10494820.2023.2197957

  • Uzunboylu, H., & Ozdamli, F. (2011). Teacher perception for m-learning: scale development and teachers’ perceptions. Journal of Computer Assisted Learning, 27(6), 544–556.

    Article  Google Scholar 

  • Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology (February 9, 2012). MIS Quarterly, 36(1), 157–178. https://ssrn.com/abstract=2002388

  • Vitalari, N. P., Venkatesh, A., & Gronhaug, K. (1985). Computing in the home: Shifts in the time allocation patterns of households. Communications of the ACM, 28(5), 512–522.

    Article  Google Scholar 

  • Voogt, J., & Roblin, N. P. (2012). A comparative analysis of international frameworks for 21st century competences: Implications for national curriculum policies. Journal of Curriculum Studies, 44(3), 299–321.

    Article  Google Scholar 

  • Wang, C., Wang, W., & Wu, H. (2020). Association between medical students’ prior experiences and perceptions of formal online education developed in response to COVID-19: a cross-sectional study in China. British Medical Journal Open, 10(10), e041886.

    Google Scholar 

  • Wang, E. S. T., & Lin, R. L. (2017). Perceived quality factors of location-based apps on trust, perceived privacy risk, and continuous usage intention. Behaviour & Information Technology, 36(1), 2–10.

    Google Scholar 

  • Yang, G. T., Yusop, F. D., & Leng, C. H. (2022). Predictors of massive open online courses (MOOC) learning satisfaction: A recipe for success. Pertanika Journal of Social Sciences, 30(4).

  • Yang, H., Cai, J., Yang, H. H., & Wang, X. (2023). Examining key factors of beginner’s continuance intention in blended learning in higher education. Journal of Computing in Higher Education, 35(1), 126–143.

    Article  Google Scholar 

  • Yang, S. (2012). Exploring college students’ attitudes and self-efficacy of mobile learning. Turkish Online Journal of Educational Technology-TOJET, 11(4), 148–154.

    Google Scholar 

  • Zheng, B., Lin, C.-H., & Kwon, J. B. (2020). The impact of learner-, instructor-, and course-level factors on online learning. Computers & Education, 150, 103851.

Download references

Acknowledgements

We thank the Research Management Centre (RMC) at Universiti Teknologi Malaysia (UTM) for allowing us to conduct this research (Q.J130000.21A2.07E10).

Funding

This work was supported by the King Saud University, Riyadh, Saudi Arabia, through Researchers Supporting Project RSP2023R417.

Author information

Authors and Affiliations

  1. Faculty of Social Sciences and Humanities, School of Education, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia

    Nisar Ahmed Dahri, Noraffandy Yahaya & Waleed Mugahed Al-Rahmi

  2. Department of Visual Arts, Arts College, King Saud University, Riyadh, 145111, Saudi Arabia

    Abeer S. Almogren

  3. Faculty of Science, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Pakistan

    Muhammad Saleem Vighio

  4. Department of Computer Science, Aalborg University, 9220, Aalborg East, Denmark

    Muhammad Saleem Vighio

Authors
  1. Nisar Ahmed Dahri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Noraffandy Yahaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Waleed Mugahed Al-Rahmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abeer S. Almogren
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Muhammad Saleem Vighio
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Nisar Ahmed Dahri or Noraffandy Yahaya.

Ethics declarations

Institutional review board statement

Not applicable.

Informed consent

Not applicable.

Conflicts of interest

The authors declare no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dahri, N.A., Yahaya, N., Al-Rahmi, W.M. et al. Investigating factors affecting teachers' training through mobile learning: Task technology fit perspective. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-023-12434-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10639-023-12434-9

Keywords

Search

Navigation